Estimation method of battery state of health based on &#34;standard sample&#34; and &#34;dual-embedded decoupling&#34;

ABSTRACT

A battery State of Health estimation method based on a standard sample and a dual-embedded decoupling includes the steps of extracting significant characteristic peaks of the standard sample, mechanism parameter calibration of the standard sample, and on-line State of Health estimation of the test battery. The battery State of Health estimation method expounds the dual coupling relationship between temperature and aging on the characteristic peak voltage of Incremental Capacity curve from the perspective of impedance characteristic mechanism analysis, and proposes a method eliminating the voltage deviation caused by the most temperature-sensitive charge transfer resistance based on the “standard sample” to realize the decoupling of the first layer. Further, when the Solid Electrolyte Interface film resistance affected by the aging and temperature coupling conforms to a linear relationship as a whole, the embedded decoupling can be realized by establishing the relation function between the linear relationship coefficient and temperature.

CROSS REFERENCE TO THE RELATED APPLICATIONS

This application is based upon and claims priority to Chinese PatentApplication No. 202110105834.9, filed on Jan. 26, 2021, the entirecontents of which are incorporated herein by reference.

TECHNICAL FIELD

The invention relates to a battery performance estimation method, inparticular to an estimation method of battery State of Health based on“standard sample” and “dual-embedded decoupling” in a wide temperaturerange, belonging to the technical field of power battery.

BACKGROUND

With the rapid development of Electric Vehicles, power batteries, themain power source of Electric Vehicles, have attracted more and moreattention. Lithium-ion battery is widely used due to high specificenergy, high specific power and so on. However, as the number of cyclesincreases, the electrode active material of a battery is graduallyconsumed. This phenomenon eventually brings battery aging and thedecline of State of Health. With the aging of the battery, the internalresistance increases and the capacity decreases. It eventually bringsthe decline of the battery performance and the increase of the breakdownrate of Electric Vehicles. Thus, accurate State of Health estimation canensure the battery safety and improve the usage performance of ElectricVehicles.

Battery State of Health estimation methods can be roughly divided intotwo categories: model-based methods and data-driven methods. Model-basedmethods need to establish a battery model to get mathematicalexpressions, and then solve the parameters of the model through someintelligent algorithms. These methods suffer from the drawback ofcomplex calculation and low utility. Data-driven methods do not requireestablish the battery model. It can overcome the shortage of model-basedmethods. The general data-driven methods include Artificial NeuralNetwork, Support Vector Machine, Gauss Process Regression, etc. Theabove mentioned methods are similar. State of Health of the given samplebattery can be predicted by using the data (such as charging anddischarging current, voltage and temperature, etc.) and thecorresponding aging state data of a given sample battery. However, onthe one hand, these methods are prone to overfitting, and on the otherhand, it takes a lot of time to pre-process the input data.

In recent years, battery State of Health estimation methods based onIncremental Capacity Analysis are increasingly studied by researchers.The battery Incremental Capacity curve, that is, the differential curveof the battery capacity-voltage curve, converts the voltage platformcontaining severe internal reaction into multiple intuitive andidentifiable peaks. By analyzing the characteristics of these peaks, thebattery aging mode and mechanism can be deduced, and then the State ofHealth of the battery can be estimated. During the actual operation, theworking conditions of the battery are more complicated. The battery willrelease a lot of heat after a long time of charging and discharging, andthe accumulated heat cannot be quickly dissipated to the outside. Thesephenomena will eventually cause the battery temperature to be muchhigher than the outside temperature. However, most of the previousresearch was conducted at a standard temperature, but the temperaturewill directly affect the chemical reaction of the battery. Many basicperformance parameters of the battery, such as capacity and internalresistance, will change at different temperatures, and the IncrementalCapacity curve will also shift to different degrees. It eventually leadsto a large difference in the accuracy of the Incremental Capacity curveto solve the State of Health at different temperatures.

SUMMARY Purpose of the Invention

Aiming at the problems existing in the prior art, the invention providesa battery State of Health estimation method based on a standard sampleand a dual-embedded decoupling. By studying the influence mechanism oftemperature and aging on the Incremental Capacity curve characteristicsof the battery, the present invention realizes the decoupling of theinfluence of temperature and aging on the Incremental Capacity curvecharacteristics, and broadens the temperature range of the IncrementalCapacity curve to solve the battery State of Health. Finally, based on“standard sample” and “dual-embedded decoupling”, the State of Healthestimation of the battery in a wide temperature range is achieved.

Technical Solution

A battery State of Health estimation method based on a standard sampleand a dual-embedded decoupling, includes the following steps:

S1: Extracting the significant characteristic peaks of the standardsample: Firstly, a same type of batteries with different aging statesare selected to conduct constant-current charging tests at the sametemperature. Then, any one of the above batteries are selected to carryout constant-current charging tests at different temperatures. Thebattery current, voltage and temperature in each test are collected.Finally, based on the above data, the Incremental Capacity curves of thebatteries with different aging states and at different temperatures aredrawn, and the significant characteristic peaks of the standard sampleare selected by analyzing the above Incremental Capacity curves.

S2. Calibration of mechanism parameters of the standard sample: Threerelation functions of the standard sample are obtained based ondual-embedded embedded decoupling.

The relation function one is a relation function between the temperatureand the voltage deviation ΔU_(ct,T) caused by the change of the chargetransfer resistance R_(ct), when the standard sample battery is notaging.

The relation function two is a linear relationship function between theaging state and the voltage deviation ΔU_(SEI,cycle,T) at the sametemperature. Among them, the voltage deviation ΔU_(SEI,cycle,T) iscaused by the Solid Electrolyte Interface film resistance aftereliminating the influence of the charge transfer resistance R_(ct).

The relation function three is a relation function between the linearrelationship coefficient and the temperature.

S3. On-line estimation of State of Health of a test battery:constant-current charging test is carried out for the test battery, andthe current, voltage and temperature T_(peak) are collected. Based onthe collected data, the Incremental Capacity curve of the test batteryis calculated and the voltage of the significant characteristic peakcorresponding to the standard sample is extracted. The State of Healthis solved according to the three relation functions of the dual-embeddeddecoupling in S2.

Further, the significant characteristic peak is a characteristic peakwith obvious relative change and located near the median voltage.

Further, in the relation function one in S2, the voltage deviationcaused by the change of the charge transfer resistance R_(ct) is thevoltage deviation ΔU_(ct,T) caused by the temperature at the significantcharacteristic peak of the standard sample battery whose State of Healthis 100%. The relation function between the temperature T and the voltagedeviation ΔU_(ct,T) of the standard sample battery is described by anArrhenius function as:

ΔU _(ct,T) =a _(ct) exp(b _(ct) /T)+c _(ct)

Where, a_(ct), b_(ct) and c_(ct) are temperature fitting parametersrelated to the charge transfer resistance.

Further, the standard sample battery with 100% State of Health is avirtual battery after the average of at least three batteries with 100%State of Health that meet factory requirements. The fitting result isquantitatively evaluated by the coefficient of determination R². Thespecific evaluation formula is as follows:

${R^{2} = {1 - \frac{\sum\limits_{i = 1}^{N}( {V_{i} - \overset{\sim}{V_{i}}} )^{2}}{\sum\limits_{i = 1}^{N}( {V_{i} - {\overset{\hat{}}{V}}_{mean}} )^{2}}}},{{\overset{\hat{}}{V}}_{mean} = {\frac{1}{N}{\sum\limits_{i = 1}^{N}V_{i}}}}$

Where, V_(i) is the measured voltage, {tilde over (V)}_(i) denotes thefitting voltage, {circumflex over (V)}_(mean) is the mean of themeasured voltage, and N denotes the amount of measured data. The closerR² is to 1, the better the fitting accuracy is.

Further, in the relation function two in S2, the linear relationshipfunction between the aging state and the voltage deviationΔU_(SEI,cycle,T) caused by the influence of the Solid ElectrolyteInterface film resistance at the same temperature can be described as:

ΔU _(SEI,cycle,T) =k _(SEI,T) SOH+b _(SEI,T)

Where, k_(SEI,T) and b_(SEI,T) are the linear relationship coefficientsrelated to the Solid Electrolyte Interface film resistance, SOH isbattery State of Health.

Further, a method to obtain the linear relationship function between theaging state and the voltage deviation ΔU_(SEI,cycle,T) after eliminatingthe influence of the charge transfer resistance is as follows: Firstly,at least three test batteries with State of Health between 100% and 80%are selected. Then the linear relationship function between the agingstate and the voltage deviation ΔU_(SEI,cycle,T) is linear fitted.Finally, a relationship between the linear relationship coefficientk_(SEI,T) and b_(SEI,T) is obtained. Among them, k_(SEI,T)=−0.01b_(SEI,T). Thus, the above linear relationship function between theaging state and the voltage deviation ΔU_(SEI,cycle,T) can be furthersimplified as:

ΔU _(SEI,cycle,T)=−0.01b _(SEI,T) SOH+b _(SEI,T)

Further, in the relation function three in S3, the relation functionbetween the linear relationship coefficient and the temperature T can bedescribed as:

k _(SEI,T) =kb _(SEI,T) =A _(SEI) exp(B _(SEI) /T)+C _(SEI)

Where, A_(SEI), B_(SEI) and C_(SEI) are the temperature fittingparameters related to the Solid Electrolyte Interface film resistance, krepresents the linear relationship coefficient between k_(SEI,T) andb_(SEI,T).

Further, a solution method of State of Health in S3 is as follows:

Firstly, a measured temperature T_(peak) is taken as input and ΔU_(ct,T)is obtained according to the relation function between the temperatureand the voltage deviation ΔU_(ct,T) in S2.

Secondly, a measured voltage U_(peak) is taken as input to obtainΔU_(SEI,cycle,T) according to ΔU_(SEI,cycle,T)=U_(peak)−ΔU_(ct,T).

Thirdly, according to the relation function between the linearrelationship coefficient and the temperature at different temperaturesin S2, the measured temperature T_(peak) is taken as the input to obtainthe linear relationship coefficient at the current temperature.

Finally, according to the linear relationship function between the agingstate and the voltage deviation ΔU_(SEI,cycle,T), the State of Health ofthe test battery is obtained.

The Benefits of the Invention

The present invention expounds a dual coupling relationship betweentemperature and aging on the characteristic peak voltage of theIncremental Capacity curve from the perspective of impedancecharacteristic mechanism analysis, and proposes a method eliminating thevoltage deviation caused by the charge transfer resistance which is mostsensitive to temperature based on the “standard sample” to realize thedecoupling of the first layer. Subsequently, in view of the voltagedeviation caused by the Solid Electrolyte Interface film resistanceaffected by aging and temperature coupling, supposing the overall aginglaw meets a linear relationship, the embedded decoupling is realized byestablishing the relation function between the linear relationshipcoefficient k_(SEI,T) and the temperature. Finally, the battery State ofHealth estimation based on Incremental Capacity curve characteristics inthe wide temperature range is realized by dual decoupling combiningfirst-layer decoupling and embedded decoupling.

The invention not only inherits the high efficiency of State of Healthestimation based on Incremental Capacity curve, but also overcomes thedefect of unclear mechanism of data-driven method. From the perspectiveof mechanism analysis, the problem of low accuracy in estimating Stateof Health based on Incremental Capacity curve in the wide temperaturerange is solved.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows the Incremental Capacity curves of batteries with differentaging states.

FIG. 2 shows the Incremental Capacity curves of a battery at differenttemperatures.

FIG. 3 shows the relation between the voltage of the characteristic peakand the temperature of the standard sample battery.

FIG. 4 shows the relation between the aging state and the voltagedeviation at different temperatures.

FIG. 5 shows the relation between the linear relationship coefficientand the temperature.

FIG. 6 shows a flow chart of the estimated method.

DETAILED DESCRIPTION OF THE EMBODIMENTS

The present invention will be further described below in combinationwith figures and a specific embodiment, but the scope of protection ofthe invention is not limited.

The present invention proposes a method for estimating a battery Stateof Health in a wide temperature range based on “standard sample” and“dual-embedded decoupling”. It mainly includes impedance characteristicanalysis, temperature and aging dual-embedded decoupling, IncrementalCapacity curve solution and characteristic analysis, online estimationof State of Health based on “standard sample”.

In the above impedance characteristic analysis, the battery impedancemainly contains the bulk resistance of the electrolyte, separator andelectrode, the Solid Electrolyte Interface film resistance and thecharge transfer resistance. It can be expressed as:

R=R _(bulk) +R _(SEI) +R _(ct)  (1)

Where, R is the battery impedance, R_(bulk) is the bulk resistance,R_(SEI) is the Solid Electrolyte Interface film resistance and R_(ct) isthe charge transfer resistance.

Battery aging causes impedance changes. The aging modes mainly includethe loss of lithium inventory and the loss of active material. Thecapacity loss rate of lithium-ion batteries for electric vehicles isgenerally less than 20%. In this use interval, the formation and growthof the Solid Electrolyte Interface film between electrode andelectrolyte leads to the loss of lithium inventory, and the increase ofthe Solid Electrolyte Interface film volume is directly proportional tothe consumption of lithium inventory. Considering that the radius of theactive particles is much larger than the thickness of the SolidElectrolyte Interface film produced on them, it can be approximatelyconsidered that the area of the Solid Electrolyte Interface filmcovering the surface of the active particle does not change in the earlystage of aging. The consumption of lithium inventory is proportional tothe change in the Solid Electrolyte Interface film thickness, and theincrease in the Solid Electrolyte Interface film thickness isproportional to the increase in R_(SEI). Thus, the change of R_(SEI) andthe degree of aging (loss of lithium inventory) can be approximatelyconsidered to a linear relationship. This phenomenon shows thatIncremental Capacity curve moves to the high voltage as a whole at acertain temperature. Meanwhile, in the early stage of aging, R_(bulk)and R_(ct) of the battery remain basically unchanged under the conditionof a constant temperature. Therefore, the impedance changes caused byaging are as follow:

ΔR _(cycle) =ΔR _(bulk,cycle) +ΔR _(SEI,cycle) +ΔR _(ct,cycle) ≈ΔR_(SEI,cycle)  (2)

Where, ΔR_(cycle) is the increase of battery impedance by agingchanging, ΔR_(bulk,cycle) is the increase of the bulk resistance byaging changing, ΔR_(SEI,cycle) is the increase of the Solid ElectrolyteInterface film resistance by aging changing, and ΔR_(ct,cycle) is theincrease of the charge transfer resistance by aging changing.

The electrolyte solution of lithium battery is lithium salt electrolyteand organic solvent, in which the conduction of electrolyte mainlydepends on ion movement. Within a certain temperature range, as thetemperature decreases, the ion activity in the battery is lower and theion migration speed decreases. It leads to an increase in the chargetransfer resistance R_(ct), and this impedance is more sensitive to lowtemperature. In the Incremental Capacity curve, it is intuitively shownthat the lateral deviation of the curve is small at high temperature,but large at low temperature. On the other hand, the temperature notonly affects the ionic conductivity in the electrolyte, but also affectsthe ionic conductivity in the Solid Electrolyte Interface film. As thetemperature increases, the ionic conductivity in the Solid ElectrolyteInterface film also increases. It is manifested as a decrease in theimpedance of the Solid Electrolyte Interface film. Thus, the effects oftemperature and aging on the impedance of the Solid ElectrolyteInterface film are coupled. Relatively speaking, the bulk resistance isrelated to battery itself and is not sensitive to temperature.Therefore, the change in the resistance of the battery caused bytemperature change can be expressed as:

ΔR _(T) =ΔR _(bulk,T) +ΔR _(SEI,T) +ΔR _(ct,T) ≈ΔR _(SEI,T) +ΔR_(ct,T)  (3)

Where, ΔR_(T) is an increase of battery impedance by temperaturechanging, ΔR_(bulk,T) is an increase of the bulk resistance bytemperature changing, ΔR_(SEI,T) is an increase of the Solid ElectrolyteInterface film resistance by temperature changing, and ΔR_(ct,T) is anincrease of the charge transfer resistance by temperature changing.

Combined Eq. (2) and (3), it can be seen that an increase in batteryimpedance caused by aging and temperature changing can be described as:

ΔR=ΔR _(SEI,T) +ΔR _(SEI,cycle) +ΔR _(ct,T)  (4)

From the above analysis, it can be seen that the charge transferresistance ΔR_(ct) is only affected by temperature and is not sensitiveto aging. However, the Solid Electrolyte Interface film resistanceΔR_(SEI) is both affected by aging and temperature. Therefore, Eq. (4)can be further expressed as:

ΔR=ΔR _(SEI,cycle,T) +ΔR _(ct,T)  (5)

Where, ΔU_(SEI,cycle,T) is an increase of resistance caused by thecoupling of temperature and aging.

In the above-mentioned temperature and aging dual-embedded decouplingmethod, the charge transfer resistance caused by temperature can bedescribed by an Arrhenius function. Meanwhile, at the same temperature,the change of the Solid Electrolyte Interface film resistance ΔR_(SEI)and the aging (loss of lithium inventory) can be approximatelyconsidered to a linear relationship. At different temperatures, theSolid Electrolyte Interface film resistance ΔR_(SEI) is related withtemperature. Because the thickness of Solid Electrolyte Interface filmis about 20 nm˜120 nm and its ion migration path is relatively short, itis more sensitive to aging. Therefore, the ΔR_(SEI,cycle,T) caused bythe coupling of aging and temperature can be considered conforms to alinear relationship, and the linear relationship coefficient k_(SEI) canbe described by an Arrhenius function. Eq. (5) can be further describedas:

$\quad\begin{matrix}\{ \begin{matrix}{R_{{SEI},{cycle}} = {{k_{SEI}{SOH}} + b_{SEI}}} \\{R_{{ct},T} = {{a_{ct}{\exp( {b_{ct}/T} )}} + c_{ct}}} \\{k_{SEI} = {{kb}_{SEI} = {{A_{SEI}{\exp( {B_{SEI}/T} )}} + C_{SEI}}}}\end{matrix}  & (6)\end{matrix}$

Where, T is the actual temperature of the battery, k_(SEI,T) andb_(SEI,T) are the linear relationship coefficient related to the SolidElectrolyte Interface film resistance, a_(ct), b_(ct) and c_(ct) aretemperature fitting parameters related to the charge transferresistance, and A_(SEI), B_(SEI) an C_(SEI) are temperature fittingparameters related to the Solid Electrolyte Interface film resistance.

Combined with Eq. (6), the voltage deviation of Incremental Capacitycurve during the constant-current charging stage can be described as:

$\quad\begin{matrix}\{ \begin{matrix}{{\Delta\; U_{{SEI},{cycle}}} = {{k_{SEI}{SOH}} + b_{SEI}}} \\{{\Delta\; U_{{ct},T}} = {{a_{ct}{\exp( {b_{ct}/T} )}} + c_{ct}}} \\{k_{SEI} = {{kb}_{SEI} = {{A_{SEI}{\exp( {B_{SEI}/T} )}} + C_{SEI}}}}\end{matrix}  & (7)\end{matrix}$

Where, ΔU_(ct,T) is voltage deviation caused by the charge transferresistance and ΔU_(SEI,cycle,T) is voltage deviation caused by the SolidElectrolyte Interface film resistance.

According to Eq. (7), firstly, for the batteries at differenttemperatures, ΔU_(ct,T) caused by the charge transfer resistance ΔR_(ct)that is most sensitive to temperature can be eliminated. Then,ΔU_(SEI,cycle,T) caused by ΔR_(SEI,cycle,T) influenced by aging andtemperature coupling can be obtained. Meanwhile, the voltage deviationis described by a linear relationship and the linear relationshipcoefficient is described by the Arrhenius function. Thus, the voltagedeviation caused by ΔR_(SEI,cycle) alone is obtained. Finally, thedual-embedded decoupling of temperature and aging on IncrementalCapacity curve is realized.

In the above Incremental Capacity curve solution and characteristicsanalysis, the Incremental Capacity curve can be solved by conventionalnumerical differentiation, polynomial fitting and probability densityfunction method, or the method in the invention patent (CN 109632138 A).The present invention only provides examples, and does not specificallylimit the solution method.

In the above Incremental Capacity curve characteristic analysis: FIG. 1shows the Incremental Capacity curve of batteries with different agingstates, and FIG. 2 shows the Incremental Capacity curve of a battery atdifferent temperatures. It can be seen from FIG. 1 that as the degree ofaging increases, the characteristic peak of the Incremental Capacitycurve shifts laterally to the right, and the peak height decreases. Itcan be seen from FIG. 2 that as the temperature rises, thecharacteristic peak of the Incremental Capacity curve laterally shiftsto the left, and the peak height rises. In the constant-current chargingstage, the voltage deviation of the Incremental Capacity curve can beapproximately attributed to the influence of impedance. The impedanceincrease caused by the decrease of temperature and the increase of agingleads to the lateral voltage shift to the right. Among the three maincharacteristic peaks of the Incremental Capacity curve, the secondcharacteristic peak has obvious relative change and is located near themedian voltage. Therefore, in the invention, the second characteristicpeak as a significant characteristic peak is selected to study thecorrelation between its characteristic change and aging/temperature.

The above online State of Health estimation based on “standard sample”can be divided into mechanism parameter calibration stage and onlineestimation stage.

The above-mentioned mechanism parameter calibration stage: firstly,different temperature and aging tests are carried out to acquire thecorresponding voltage, current and temperature. Then, the IncrementalCapacity curve is calculated to obtain second characteristic peakvoltage. Finally, the parameters in three mechanism relationships arecalibrated by Eq. (7). Three calibration mechanism relationshipsinclude: 1) For the unaged battery, the relation function between thetemperature and the voltage deviation caused by the change of R_(ct); 2)At the same temperature, the linear relationship function between theaging state and the voltage deviation after eliminating the influence ofR_(ct); 3) The relation function between the linear relationshipcoefficient and the temperature.

The above calibration mechanism relationship 1): When the battery is notaging, the voltage deviation caused by the change of R_(ct) is obtainedbased on the “standard sample”. The “standard sample” battery is avirtual battery after the average of at least three batteries that meetthe factory requirements (State of Health is 100%). The basic parametersof the tested battery used in the invention are shown in Table 1. Ofcourse, this is only for the purpose of expound relevant theories, andthe number of batteries and basic battery parameters are not limited.The State of Health of the standard sample battery at differenttemperatures is 100%, that is, the battery is not aging. At this time,the voltage deviation of the second characteristic peak caused bytemperature is theoretically affected by the change of R_(ct). FIG. 3shows the second characteristic peak voltage of the standard samplebattery at different temperatures and the fitting results by theArrhenius function. Table 2 shows the fitting parameters of theArrhenius function. In order to quantitatively evaluate the quality ofthe fitting results, the R² is calculated, which can be expressed as:

$\begin{matrix}{{R^{2} = {1 - \frac{\sum\limits_{i = 1}^{N}( {V_{i} - \overset{\sim}{V_{i}}} )^{2}}{\sum\limits_{i = 1}^{N}( {V_{i} - {\overset{\hat{}}{V}}_{mean}} )^{2}}}},{{\overset{\hat{}}{V}}_{mean} = {\frac{1}{N}{\sum\limits_{i = 1}^{N}V_{i}}}}} & (8)\end{matrix}$

Where, V_(i) is the measured voltage, {tilde over (V)}_(i) denotes thefitting voltage, {circumflex over (V)}_(mean) is the mean of themeasured voltage, and N denotes the amount of measured data. The closerR² is to 1, the better the fitting accuracy is. It can be from the Tablethat R² is 0.996. Obviously, the Arrhenius function can better describethe voltage deviation caused by temperature.

For batteries with different aging states, the measured temperature isbrought into the Arrhenius function to obtain the voltage deviationcaused by temperature firstly. Then, the voltage deviation caused by thecorresponding temperature be subtracted from the measured voltage of thecharacteristic peak to eliminate the effect of R_(ct).

TABLE 1 Parameters of the tested battery Item Parameter Rated capacity1.3 Ah Rated voltage 3.2 V Voltage range 2.5~3.65 V Size 18 mm (D)*65 mm(T)

TABLE 2 Fitting coefficients of the Arrhenius function for voltagedeviation caused by temperature a_(ct) b_(ct) c_(ct) R-square3.444*10⁻¹⁴ 6313.57 3.331 0.996

The above calibration mechanism relationship 2) is the linearrelationship function between the aging state and the voltage deviation:The present invention selects three batteries with State of Health of100%, 88% and 82% respectively to analyze the linear relationshipbetween the aging state and the voltage deviation. Of course, this isonly for the purpose of expound relevant theories, and the number ofbatteries and basic battery parameters are not limited. Among them, thevoltage deviation is the result of eliminating the influence of R_(ct),as shown in FIG. 4. In the early aging stage, the aging state andimpedance have an approximate linear relationship, and the slightdifference may mainly come from the simplified processing of the complexcomponents of the Solid Electrolyte Interface film. The correspondinglinear relationship fitting curve is also shown in FIG. 4. Table 3 givesthe linear relationship coefficient at different temperatures. It can beseen from the table that k_(SEI,T) and b_(SEI,T) have a linearrelationship, k_(SEI,T)=−0.01 b_(SEI,T), so the above linearrelationship function can be further simplified as:

ΔU _(SEI,cycle)=−0.01b _(SEI) SOH+b _(SEI)  (9)

The above calibration mechanism relationship 3) is the relation functionbetween the linear relationship coefficient and temperature at differenttemperatures: From the above-mentioned temperature and agingdual-embedded decoupling analysis, the linear relationship coefficientk_(SEI,T), b_(SEI,T) and temperature conforms to the Arrhenius function.The coefficients b_(SEI,T) in Table 3 are fitted, and the specificresults are shown in FIG. 5 and the fitting parameters are shown inTable 4. The R² is 0.994, indicating that the curve has a good fittingdegree.

TABLE 3 Linear relationship coefficient at different temperaturesTemperature/K k_(SEI) b_(SEI) R-square 268 −0.00231 0.22964 0.955 273−0.00182 0.18077 0.984 278 −0.00134 0.13303 0.956 283 −9.25E−04 0.091930.970 288 −6.88E−04 0.06835 0.962 293 −5.90E−04 0.05852 0.937 298−4.52E−04 0.04482 0.942 303 −3.89E−04 0.03866 0.960 308 −3.22E−040.03209 0.990 313 −3.01E−04 0.03007 0.999 318 −3.30E−04 0.03293 0.992323 −2.91E−04 0.02909 0.999 328 −2.71E−04 0.02720 0.993

TABLE 4 Fitting parameters of the linear relationship coefficientA_(SEI) B_(SEI) C_(SEI) R-square 7.6246*10⁻¹⁰ 5218.66 0.0179 0.994

The online estimation stage: The voltage, current and temperature of ameasured battery during the charging process are collected by BatteryManagement System (BMS), and the Incremental Capacity curve of themeasured battery is then solved and the second characteristic peakvoltage is extracted. Based on the extracted the second characteristicpeak voltage and the calibrated mechanism relationships, the State ofHealth of the measured battery is eventually estimated. The specificState of Health estimated process is as follows: Firstly, the measuredtemperature is taken as the input to obtain the voltage deviation causedby R_(ct), and the standard voltage deviation is obtained by subtractingthe voltage deviation caused by R_(ct) from the measured secondcharacteristic peak voltage. Then, the temperature is brought into theArrhenius function to calculated the linear relationship coefficientk_(SEI,T) and b_(SEI,T). Finally, the State of Health is solved based onthe calibration mechanism relationship between the linear relationshipcoefficient and the standard voltage deviation, the online State ofHealth estimation in a wide temperature range is realized. The specificprocess is shown in FIG. 6. Here is an example to illustrate:

Taken the measured voltage of the second characteristic peak U_(peak)and temperature T_(peak) as input and State of Health as output, theestimated expression is as follows:

$\quad\begin{matrix}\{ \begin{matrix}{{\Delta\; U_{{SEI},{cycle},T}} = {U_{B,{peak}} - U_{{ct},T}}} \\{U_{{ct},T} = {{3.444 \times 1{0^{- 14} \cdot {\exp( \frac{6313.57}{T} )}}} + 3.331}} \\{b_{{SEI},T} = {{{- 100} \cdot k_{{SEI},T}} = {{7.6246 \times 1{0^{- 10} \cdot {\exp( \frac{521{8.6}6}{T} )}}} + {0{.0179}}}}} \\{{SOH} = \frac{{\Delta U}_{{SEI},{cycle},T} - b_{{SEI},T}}{{- 0.01}b_{{SEI},T}}}\end{matrix}  & (10)\end{matrix}$

The State of Health of No. 1˜3 batteries at different temperatures isobtained, and the estimated results of State of Health are shown inTable 5. It can be seen from the table that the relative error is 4.03%.The average relative errors of No. 2 battery and No. 3 battery are 2.41%and 1.21%, respectively. It shows that the proposed method can realizethe State of Health estimation of the measured battery in the widetemperature range.

TABLE 5 Estimated State of Health of different aging batteries PredictedState of Predicted State of Temperature Health Relative Health Relative(K) (Measured 88%) error (Measured 82%) error 268 91.21% 3.21% 81.63%−0.37% 273 89.10% 1.10% 80.39% −1.61% 278 90.42% 2.42% 80.10% −1.90% 28390.77% 2.77% 82.01% 0.01% 288 91.45% 3.45% 82.70% 0.70% 293 91.64% 3.64%81.20% −0.80% 298 92.03% 4.03% 82.47% 0.47% 303 91.29% 3.29% 82.20%0.20% 308 90.30% 2.30% 83.28% 1.28% 313 88.57% 0.57% 82.60% 0.60% 31887.32% −0.68% 78.50% −3.50% 323 86.91% −1.09% 79.59% −2.41% 328 85.20%−2.80% 80.06% −1.94%

The embodiments are the preferred embodiments of the present invention,but the present invention is not limited to the above-mentionedembodiments. Without departing from the essence of the presentinvention, any obvious improvements, substitutions or modifications thatcan be made by those skilled in the art fall within the protection scopeof the present invention.

What is claimed is:
 1. A battery State of Health estimation method basedon a standard sample and a dual-embedded decoupling, comprising thefollowing steps: S1: extracting significant characteristic peaks of thestandard sample; wherein firstly, a same type of batteries withdifferent aging states are selected to conduct constant-current chargingtests at a same temperature; then, any one of the above batteries isselected to carry out constant-current charging tests at differenttemperatures, and battery current, voltage and temperature in each testare collected; finally, based on the above data, Incremental Capacitycurves of batteries with different aging states and at differenttemperatures are drawn, and the significant characteristic peaks of thestandard sample are selected by analyzing the above Incremental Capacitycurves; S2: calibration of mechanism parameters of the standard sample;wherein relation functions of the standard sample are obtained based onthe dual-embedded decoupling; wherein a first relation function is arelation function between the temperature and a voltage deviationΔU_(ct,T) caused by a change of a charge transfer resistance R_(ct),when the standard sample battery is not aging; a second relationfunction is a linear relationship function between the aging state andthe voltage deviation ΔU_(SEI,cycle,T) at the same temperature; whereinthe voltage deviation ΔU_(SEI,cycle,T) is caused by the SolidElectrolyte Interface film resistance after eliminating an influence ofthe charge transfer resistance R_(ct); a third relation function is arelationship function between a linear relationship coefficient and thetemperature; S3: on-line estimation of State of Health of a testbattery; wherein constant-current charging test is carried out for thetest battery, and the current, voltage and temperature T_(peak) arecollected; based on the collected data, the Incremental Capacity curveof the test battery is calculated and the voltage of the significantcharacteristic peak corresponding to the standard sample is extracted;the State of Health is solved step by step according to the threerelation functions of the double-embedded decoupling in S2.
 2. Thebattery State of Health estimation method based on the standard sampleand the dual-embedded decoupling according to claim 1, wherein thesignificant characteristic peak is a characteristic peak with obviousrelative change and located near a median voltage.
 3. The battery Stateof Health estimation method based on the standard sample and thedual-embedded decoupling according to claim 1, wherein, in the firstrelation function in S2, the voltage deviation caused by the change ofthe charge transfer resistance R_(ct) is the voltage deviation ΔU_(ct,T)caused by the temperature at the significant characteristic peak of thestandard sample battery whose State of Health is 100%, when the batteryis not aging; the relation function between the temperature T and thevoltage deviation ΔU_(ct,T) of the standard sample battery is describedby an Arrhenius function as:ΔU _(ct,T) =a _(ct) exp(b _(ct) /T)+c _(ct) wherein, a_(ct), b_(ct) andc_(ct) are temperature fitting parameters related to the charge transferresistance R_(ct).
 4. The battery State of Health estimation methodbased on the standard sample and the dual-embedded decoupling accordingto claim 3, wherein, the standard sample battery with 100% State ofHealth is a virtual battery after an average of at least three batterieswith 100% State of Health that meet factory requirements; a fittingresult is quantitatively evaluated by the coefficient of determinationR²; a specific evaluation formula is as follows:${R^{2} = {1 - \frac{\sum\limits_{i = 1}^{N}( {V_{i} - \overset{\sim}{V_{i}}} )^{2}}{\sum\limits_{i = 1}^{N}( {V_{i} - {\overset{\hat{}}{V}}_{mean}} )^{2}}}},{{\overset{\hat{}}{V}}_{mean} = {\frac{1}{N}{\sum\limits_{i = 1}^{N}V_{i}}}}$wherein, V_(i) is a measured voltage, {tilde over (V)}_(i) denotes afitting voltage, {circumflex over (V)}_(mean) is a mean of the measuredvoltage, and N denotes an amount of measured data; the closer R² is to1, the better the fitting accuracy is.
 5. The battery State of Healthestimation method based on the standard sample and the dual-embeddeddecoupling according to claim 1, wherein, in the second relationfunction in S2, the linear relationship function between the aging stateand the voltage deviation ΔU_(SEI,cycle,T) caused by an influence of theSolid Electrolyte Interface film resistance at the same temperature isdescribed as:ΔU _(SEI,cycle,T) =k _(SEI,T) SOH+b _(SEI,T) wherein, k_(SEI,T) andb_(SEI,T) are linear relationship coefficients related to the SolidElectrolyte Interface film resistance, SOH is battery State of Health.6. The battery State of Health estimation method based on the standardsample and the dual-embedded decoupling according to claim 5, wherein, amethod to obtain the linear relationship function between the agingstate and the voltage deviation after eliminating the influence ofR_(ct) is as follows: firstly, at least three test batteries with Stateof Health between 100% and 80% are selected; then the linearrelationship function between the aging state and voltage deviationΔU_(SEI,cycle,T) is linear fitted; finally, a relationship betweenlinear fitting parameters k_(SEI,T) and b_(SEI,T) is obtained; whereink_(SEI,T)=−0.01 b_(SEI,T), thus, the above linear relationship functionbetween the aging state and the voltage deviation ΔU_(SEI,cycle,T) isfurther simplified as:ΔU _(SEI,cycle,T)=−0.01b _(SEI,T) SOH+b _(SEI,T).
 7. The battery Stateof Health estimation method based on standard sample and dual-embeddeddecoupling according to claim 1, wherein, in the third relation functionin S3, the relation function between the linear relationship coefficientand the temperature T is described as:k _(SEI,T) =kb _(SEI,T) =A _(SEI) exp(B _(SEI) /T)+C _(SEI) wherein,A_(SEI), B_(SEI) and C_(SEI) are temperature fitting parameters relatedto the Solid Electrolyte Interface film resistance; k represents thelinear relationship coefficient between k_(SEI,T) and b_(SEI,T).
 8. Thebattery State of Health estimation method based on the standard sampleand the dual-embedded decoupling according to claim 1, wherein, asolution method of State of Health in S3 is as follows: first, ameasured temperature T_(peak) is taken as input and ΔU_(ct,T) isobtained according to the relation function between the temperature andthe voltage deviation ΔU_(ct,T) in S2; secondly, a measured voltageU_(peak) is taken as input to obtain ΔU_(SEI,cycle,T) according toΔU_(SEI,cycle,T)=U_(peak)−ΔU_(ct,T); thirdly, according to the relationfunction between the linear relationship coefficient and the temperatureat different temperatures in S2, the measured temperature T_(peak) istaken as the input to obtain the linear relationship coefficient at acurrent temperature; finally, according to the linear relationshipfunction between the aging state and the voltage deviationΔU_(SEI,cycle,T), the State of Health of the test battery is obtained.